Traditional ML research assumes that both the training and test data share the same feature space and data distribution. When this is the case, the source and target domains are equal, i.e. \(\mathcal {D}_S = \mathcal {D}_T\) , as well as the source and target tasks, i.e. \(\mathcal {T}_S=\mathcal {T}_T\)  [1]}. A well-known phenomenon when working with real-world datasets and applications is the occurrence of dataset shift or domain shift. This refers to differences in data distributions over time or between domains [2]}. Due to this phenomenon, models trained on one domain can not be directly transferred to a related but different domain without decreasing the model's performance. One cumbersome option would be to recollect new training data, e.g. performing new forest inventories and perform new flight campaigns, to acquire updated AGB samples and ALS data. Given access to new training data, the entire ML model could then be retrained. TL would in this setting be more appealing than traditional ML, as this paradigm allows either \(\mathcal {D}_S \ne \mathcal {D}_T\)  or \(\mathcal {T}_S \ne \mathcal {T}_T\)  [1]}. From the definition of Pan and Yang in [1]}, TL aims to improve the training of a target predictive function \(f_T(\cdot )\)  in \(\mathcal {D}_T\)  using knowledge from the related source domain \(\mathcal {D}_S\)  and a related source task \(\mathcal {T}_S\) .
In this setting, \(\mathcal {D}_S \ne \mathcal {D}_T\)  implies that either the feature spaces or the marginal probability distributions of \(\mathcal {D}_S\)  and \(\mathcal {D}_T\)  can be different, while \(\mathcal {T}_S \ne \mathcal {T}_T\)  implies that either the feature spaces or the conditional probabilities of \(\mathcal {T}_S\)  and \(\mathcal {T}_T\)  differ. DA is related to TL, and the term is sometimes used interchangeably with TL in the literature [5]}. In this paper, we formalise DA as the situation when \(\mathcal {T}_S=\mathcal {T}_T\)  while \(\mathcal {D}_S \ne \mathcal {D}_T\) . Thus, in DA, the source domain is altered such that its distribution resembles the target distribution.
